Konstantinov, Nikola H., and Christoph Lampert. “Fairness-Aware PAC Learning from Corrupted Data.” Journal of Machine Learning Research, vol. 23, ML Research Press, 2022, pp. 1–60.
LibreCat: https://research-explorer.ista.ac.at/record/10802
arXiv: 2102.06004

Konstantinov, Nikola H., and Christoph Lampert. “On the Impossibility of Fairness-Aware Learning from Corrupted Data.” Proceedings of Machine Learning Research, vol. 171, ML Research Press, 2022, pp. 59–83.
LibreCat: https://research-explorer.ista.ac.at/record/13241
arXiv: 2102.06004

Konstantinov, Nikola H. Robustness and Fairness in Machine Learning. Institute of Science and Technology Austria, 2022, doi:10.15479/at:ista:10799.
LibreCat: https://research-explorer.ista.ac.at/record/10799
DOI: 10.15479/at:ista:10799

Volhejn, Vaclav, and Christoph Lampert. “Does SGD Implicitly Optimize for Smoothness?”10.1007/978-3-030-71278-5_18__42nd German Conference on Pattern Recognition, vol. 12544, Springer, 2021, pp. 246–59, doi:10.1007/978-3-030-71278-5_18.
LibreCat: https://research-explorer.ista.ac.at/record/9210
DOI: 10.1007/978-3-030-71278-5_18

Phuong, Mary, and Christoph Lampert. “The Inductive Bias of ReLU Networks on Orthogonally Separable Data.” 9th International Conference on Learning Representations, 2021.
LibreCat: https://research-explorer.ista.ac.at/record/9416

Konstantinov, Nikola H., and Christoph Lampert. “Fairness through Regularization for Learning to Rank.”10.48550/arXiv.2102.05996__ArXiv, 2102.05996, doi:10.48550/arXiv.2102.05996.
LibreCat: https://research-explorer.ista.ac.at/record/10803
DOI: 10.48550/arXiv.2102.05996
arXiv: 2102.05996

Phuong, Mary. Underspecification in Deep Learning. Institute of Science and Technology Austria, 2021, doi:10.15479/AT:ISTA:9418.
LibreCat: https://research-explorer.ista.ac.at/record/9418
DOI: 10.15479/AT:ISTA:9418

Lampert, Christoph. “Zero-Shot Learning.”10.1007/978-3-030-63416-2_874__Computer Vision, edited by Katsushi Ikeuchi, 2nd ed., Springer, 2021, pp. 1395–97, doi:10.1007/978-3-030-63416-2_874.
LibreCat: https://research-explorer.ista.ac.at/record/14987
DOI: 10.1007/978-3-030-63416-2_874

Anciukevicius, Titas, et al. “Object-Centric Image Generation with Factored Depths, Locations, and Appearances.” ArXiv, 2004.00642.
LibreCat: https://research-explorer.ista.ac.at/record/8063
arXiv: 2004.00642

Henderson, Paul M., and Christoph Lampert. “Unsupervised Object-Centric Video Generation and Decomposition in 3D.” 34th Conference on Neural Information Processing Systems, vol. 33, Curran Associates, 2020, pp. 3106–3117.
LibreCat: https://research-explorer.ista.ac.at/record/8188
arXiv: 2007.06705

Henderson, Paul M., and Vittorio Ferrari. “Learning Single-Image 3D Reconstruction by Generative Modelling of Shape, Pose and Shading.”10.1007/s11263-019-01219-8__International Journal of Computer Vision, vol. 128, Springer Nature, 2020, pp. 835–54, doi:10.1007/s11263-019-01219-8.
LibreCat: https://research-explorer.ista.ac.at/record/6952
DOI: 10.1007/s11263-019-01219-8
WoS: 000491042100002
arXiv: 1901.06447

Royer, Amélie, and Christoph Lampert. “Localizing Grouped Instances for Efficient Detection in Low-Resource Scenarios.”10.1109/WACV45572.2020.9093288__IEEE Winter Conference on Applications of Computer Vision, 1716–1725, IEEE, 2020, doi:10.1109/WACV45572.2020.9093288.
LibreCat: https://research-explorer.ista.ac.at/record/7936
DOI: 10.1109/WACV45572.2020.9093288
arXiv: 2004.12623

Royer, Amélie, and Christoph Lampert. “A Flexible Selection Scheme for Minimum-Effort Transfer Learning.”10.1109/WACV45572.2020.9093635__2020 IEEE Winter Conference on Applications of Computer Vision, 2180–2189, IEEE, 2020, doi:10.1109/WACV45572.2020.9093635.
LibreCat: https://research-explorer.ista.ac.at/record/7937
DOI: 10.1109/WACV45572.2020.9093635
arXiv: 2008.11995

Royer, Amélie, et al. “XGAN: Unsupervised Image-to-Image Translation for Many-to-Many Mappings.”10.1007/978-3-030-30671-7_3__Domain Adaptation for Visual Understanding, edited by Richa Singh et al., Springer Nature, 2020, pp. 33–49, doi:10.1007/978-3-030-30671-7_3.
LibreCat: https://research-explorer.ista.ac.at/record/8092
DOI: 10.1007/978-3-030-30671-7_3
arXiv: 1711.05139

Phuong, Mary, and Christoph Lampert. “Functional vs. Parametric Equivalence of ReLU Networks.” 8th International Conference on Learning Representations, 2020.
LibreCat: https://research-explorer.ista.ac.at/record/7481

Konstantinov, Nikola H., et al. “On the Sample Complexity of Adversarial Multi-Source PAC Learning.” Proceedings of the 37th International Conference on Machine Learning, vol. 119, ML Research Press, 2020, pp. 5416–25.
LibreCat: https://research-explorer.ista.ac.at/record/8724
arXiv: 2002.10384

Royer, Amélie. Leveraging Structure in Computer Vision Tasks for Flexible Deep Learning Models. Institute of Science and Technology Austria, 2020, doi:10.15479/AT:ISTA:8390.
LibreCat: https://research-explorer.ista.ac.at/record/8390
DOI: 10.15479/AT:ISTA:8390

Henderson, Paul M., et al. “Leveraging 2D Data to Learn Textured 3D Mesh Generation.”10.1109/CVPR42600.2020.00752__Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, IEEE, 2020, pp. 7498–507, doi:10.1109/CVPR42600.2020.00752.
LibreCat: https://research-explorer.ista.ac.at/record/8186
DOI: 10.1109/CVPR42600.2020.00752
arXiv: 2004.04180

Sun, Rémy, and Christoph Lampert. “KS(Conf): A Light-Weight Test If a Multiclass Classifier Operates Outside of Its Specifications.”10.1007/s11263-019-01232-x__International Journal of Computer Vision, vol. 128, no. 4, Springer Nature, 2020, pp. 970–95, doi:10.1007/s11263-019-01232-x.
LibreCat: https://research-explorer.ista.ac.at/record/6944
DOI: 10.1007/s11263-019-01232-x
WoS: 000494406800001

Kersting, Kristian, et al., editors. Wie Maschinen Lernen: Künstliche Intelligenz Verständlich Erklärt. 1st ed., Springer Nature, 2019, doi:10.1007/978-3-658-26763-6.
LibreCat: https://research-explorer.ista.ac.at/record/7171
DOI: 10.1007/978-3-658-26763-6

Ashok, Pranav, et al. “Strategy Representation by Decision Trees with Linear Classifiers.”10.1007/978-3-030-30281-8_7__16th International Conference on Quantitative Evaluation of Systems, vol. 11785, Springer Nature, 2019, pp. 109–28, doi:10.1007/978-3-030-30281-8_7.
LibreCat: https://research-explorer.ista.ac.at/record/6942
DOI: 10.1007/978-3-030-30281-8_7
WoS: 000679281300007
arXiv: 1906.08178

Xian, Yongqin, et al. “Zero-Shot Learning - A Comprehensive Evaluation of the Good, the Bad and the Ugly.”10.1109/tpami.2018.2857768__IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 41, no. 9, Institute of Electrical and Electronics Engineers (IEEE), 2019, pp. 2251–65, doi:10.1109/tpami.2018.2857768.
LibreCat: https://research-explorer.ista.ac.at/record/6554
DOI: 10.1109/tpami.2018.2857768
WoS: 000480343900015
arXiv: 1707.00600

Phuong, Mary, and Christoph Lampert. “Distillation-Based Training for Multi-Exit Architectures.”10.1109/ICCV.2019.00144__IEEE International Conference on Computer Vision, vol. 2019–October, IEEE, 2019, pp. 1355–64, doi:10.1109/ICCV.2019.00144.
LibreCat: https://research-explorer.ista.ac.at/record/7479
DOI: 10.1109/ICCV.2019.00144
WoS: 000531438101047

Kolesnikov, Alexander, et al. “Detecting Visual Relationships Using Box Attention.”10.1109/ICCVW.2019.00217__Proceedings of the 2019 International Conference on Computer Vision Workshop, 1749–1753, IEEE, 2019, doi:10.1109/ICCVW.2019.00217.
LibreCat: https://research-explorer.ista.ac.at/record/7640
DOI: 10.1109/ICCVW.2019.00217
WoS: 000554591601098
arXiv: 1807.02136

Phuong, Mary, and Christoph Lampert. “Towards Understanding Knowledge Distillation.” Proceedings of the 36th International Conference on Machine Learning, vol. 97, ML Research Press, 2019, pp. 5142–51.
LibreCat: https://research-explorer.ista.ac.at/record/6569

Konstantinov, Nikola H., and Christoph Lampert. “Robust Learning from Untrusted Sources.” Proceedings of the 36th International Conference on Machine Learning, vol. 97, ML Research Press, 2019, pp. 3488–98.
LibreCat: https://research-explorer.ista.ac.at/record/6590
arXiv: 1901.10310

Sun, Rémy, and Christoph Lampert. KS(Conf): A Light-Weight Test If a ConvNet Operates Outside of Its Specifications. Vol. 11269, Springer Nature, 2019, pp. 244–59, doi:10.1007/978-3-030-12939-2_18.
LibreCat: https://research-explorer.ista.ac.at/record/6482
DOI: 10.1007/978-3-030-12939-2_18
arXiv: 1804.04171

Zimin, Alexander. Learning from Dependent Data. Institute of Science and Technology Austria, 2018, doi:10.15479/AT:ISTA:TH1048.
LibreCat: https://research-explorer.ista.ac.at/record/68
DOI: 10.15479/AT:ISTA:TH1048

Kolesnikov, Alexander. Weakly-Supervised Segmentation and Unsupervised Modeling of Natural Images. Institute of Science and Technology Austria, 2018, doi:10.15479/AT:ISTA:th_1021.
LibreCat: https://research-explorer.ista.ac.at/record/197
DOI: 10.15479/AT:ISTA:th_1021

Ringbauer, Harald, et al. “Estimating Barriers to Gene Flow from Distorted Isolation-by-Distance Patterns.”10.1534/genetics.117.300638__Genetics, vol. 208, no. 3, Genetics Society of America, 2018, pp. 1231–45, doi:10.1534/genetics.117.300638.
LibreCat: https://research-explorer.ista.ac.at/record/563
DOI: 10.1534/genetics.117.300638
WoS: 000426219600025

Darrell, Trevor, et al. “Guest Editors’ Introduction to the Special Section on Learning with Shared Information for Computer Vision and Multimedia Analysis.”10.1109/TPAMI.2018.2804998__IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 40, no. 5, IEEE, 2018, pp. 1029–31, doi:10.1109/TPAMI.2018.2804998.
LibreCat: https://research-explorer.ista.ac.at/record/321
DOI: 10.1109/TPAMI.2018.2804998
WoS: 000428901200001

Uijlings, Jasper, et al. “Learning Intelligent Dialogs for Bounding Box Annotation.”10.1109/cvpr.2018.00956__2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, IEEE, 2018, pp. 9175–84, doi:10.1109/cvpr.2018.00956.
LibreCat: https://research-explorer.ista.ac.at/record/10882
DOI: 10.1109/cvpr.2018.00956
WoS: 000457843609036
arXiv: 1712.08087

Sahoo, Subham, et al. “Learning Equations for Extrapolation and Control.” Proceedings of the 35th International Conference on Machine Learning, vol. 80, ML Research Press, 2018, pp. 4442–50.
LibreCat: https://research-explorer.ista.ac.at/record/6012
WoS: 000683379204058
arXiv: 1806.07259

Kuzborskij, Ilja, and Christoph Lampert. “Data-Dependent Stability of Stochastic Gradient Descent.” Proceedings of the 35 Th International Conference on Machine Learning, vol. 80, ML Research Press, 2018, pp. 2815–24.
LibreCat: https://research-explorer.ista.ac.at/record/6011
WoS: 000683379202095
arXiv: 1703.01678

Alistarh, Dan-Adrian, et al. “The Convergence of Sparsified Gradient Methods.” Advances in Neural Information Processing Systems 31, vol. Volume 2018, Neural Information Processing Systems Foundation, 2018, pp. 5973–83.
LibreCat: https://research-explorer.ista.ac.at/record/6589
WoS: 000461852000047
arXiv: 1809.10505

Der, Ralf, and Georg S. Martius. Dynamical Self Consistency Leads to Behavioral Development and Emergent Social Interactions in Robots. 7846789, IEEE, 2017, doi:10.1109/DEVLRN.2016.7846789.
LibreCat: https://research-explorer.ista.ac.at/record/652
DOI: 10.1109/DEVLRN.2016.7846789

Der, Ralf, and Georg S. Martius. “Self Organized Behavior Generation for Musculoskeletal Robots.”10.3389/fnbot.2017.00008__Frontiers in Neurorobotics, vol. 11, no. MAR, 00008, Frontiers Research Foundation, 2017, doi:10.3389/fnbot.2017.00008.
LibreCat: https://research-explorer.ista.ac.at/record/658
DOI: 10.3389/fnbot.2017.00008

Martius, Georg S., and Christoph Lampert. “Extrapolation and Learning Equations.” 5th International Conference on Learning Representations, ICLR 2017 - Workshop Track Proceedings, International Conference on Learning Representations, 2017.
LibreCat: https://research-explorer.ista.ac.at/record/6841
arXiv: 1610.02995

Pielorz, Jasmin, et al. “Optimal Geospatial Volunteer Allocation Needs Realistic Distances.”10.1109/BigData.2017.8258375__2017 IEEE International Conference on Big Data, IEEE, 2017, pp. 3760–63, doi:10.1109/BigData.2017.8258375.
LibreCat: https://research-explorer.ista.ac.at/record/750
DOI: 10.1109/BigData.2017.8258375

Kolesnikov, Alexander, and Christoph Lampert. “PixelCNN Models with Auxiliary Variables for Natural Image Modeling.” 34th International Conference on Machine Learning, vol. 70, JMLR, 2017, pp. 1905–14.
LibreCat: https://research-explorer.ista.ac.at/record/1000
WoS: 000683309501102
arXiv: 1612.08185

Rebuffi, Sylvestre Alvise, et al. ICaRL: Incremental Classifier and Representation Learning. Vol. 2017, IEEE, 2017, pp. 5533–42, doi:10.1109/CVPR.2017.587.
LibreCat: https://research-explorer.ista.ac.at/record/998
DOI: 10.1109/CVPR.2017.587
WoS: 000418371405066

Royer, Amélie, et al. Probabilistic Image Colorization. BMVA Press, 2017, p. 85.1-85.12, doi:10.5244/c.31.85.
LibreCat: https://research-explorer.ista.ac.at/record/911
DOI: 10.5244/c.31.85
arXiv: 1705.04258

Zimin, Alexander, and Christoph Lampert. Learning Theory for Conditional Risk Minimization. Vol. 54, ML Research Press, 2017, pp. 213–22.
LibreCat: https://research-explorer.ista.ac.at/record/1108
WoS: 000509368500024

Pentina, Anastasia, and Christoph Lampert. Multi-Task Learning with Labeled and Unlabeled Tasks. Vol. 70, ML Research Press, 2017, pp. 2807–16.
LibreCat: https://research-explorer.ista.ac.at/record/999
WoS: 000683309502093

Pentina, Anastasia, and Ruth Urner. Lifelong Learning with Weighted Majority Votes. Vol. 29, Neural Information Processing Systems, 2016, pp. 3619–27.
LibreCat: https://research-explorer.ista.ac.at/record/1098

Kolesnikov, Alexander, and Christoph Lampert. “Improving Weakly-Supervised Object Localization by Micro-Annotation.”10.5244/C.30.92__Proceedings of the British Machine Vision Conference 2016, vol. 2016–September, BMVA Press, 2016, p. 92.1-92.12, doi:10.5244/C.30.92.
LibreCat: https://research-explorer.ista.ac.at/record/1102
DOI: 10.5244/C.30.92

Martius, Georg S., et al. Compliant Control for Soft Robots: Emergent Behavior of a Tendon Driven Anthropomorphic Arm. Vol. 2016–November, 7759138, IEEE, 2016, doi:10.1109/IROS.2016.7759138.
LibreCat: https://research-explorer.ista.ac.at/record/1214
DOI: 10.1109/IROS.2016.7759138

Kolesnikov, Alexander, and Christoph Lampert. Seed, Expand and Constrain: Three Principles for Weakly-Supervised Image Segmentation. Vol. 9908, Springer, 2016, pp. 695–711, doi:10.1007/978-3-319-46493-0_42.
LibreCat: https://research-explorer.ista.ac.at/record/1369
DOI: 10.1007/978-3-319-46493-0_42

Pielorz, Jasmin, and Christoph Lampert. Optimal Geospatial Allocation of Volunteers for Crisis Management. 7402041, IEEE, 2016, doi:10.1109/ICT-DM.2015.7402041.
LibreCat: https://research-explorer.ista.ac.at/record/1707
DOI: 10.1109/ICT-DM.2015.7402041

Martius, Georg S., et al. “Self-Organized Control of an Tendon Driven Arm by Differential Extrinsic Plasticity.”10.7551/978-0-262-33936-0-ch029__Proceedings of the Artificial Life Conference 2016, vol. 28, MIT Press, 2016, pp. 142–43, doi:10.7551/978-0-262-33936-0-ch029.
LibreCat: https://research-explorer.ista.ac.at/record/8094
DOI: 10.7551/978-0-262-33936-0-ch029

Pentina, Anastasia. Theoretical Foundations of Multi-Task Lifelong Learning. Institute of Science and Technology Austria, 2016, doi:10.15479/AT:ISTA:TH_776.
LibreCat: https://research-explorer.ista.ac.at/record/1126
DOI: 10.15479/AT:ISTA:TH_776

Pentina, Anastasia, and Christoph Lampert. Lifelong Learning with Non-i.i.d. Tasks. Vol. 2015, Neural Information Processing Systems, 2015, pp. 1540–48.
LibreCat: https://research-explorer.ista.ac.at/record/1425

Xia, Wei, et al. “Segmentation over Detection via Optimal Sparse Reconstructions.”10.1109/TCSVT.2014.2379972__IEEE Transactions on Circuits and Systems for Video Technology, vol. 25, no. 8, IEEE, 2015, pp. 1295–308, doi:10.1109/TCSVT.2014.2379972.
LibreCat: https://research-explorer.ista.ac.at/record/1533
DOI: 10.1109/TCSVT.2014.2379972

Der, Ralf, and Georg S. Martius. “Novel Plasticity Rule Can Explain the Development of Sensorimotor Intelligence.”10.1073/pnas.1508400112__PNAS, vol. 112, no. 45, National Academy of Sciences, 2015, pp. 24–32, doi:10.1073/pnas.1508400112.
LibreCat: https://research-explorer.ista.ac.at/record/1570
DOI: 10.1073/pnas.1508400112
PMID: 26504200

Pentina, Anastasia, and Shai Ben David. Multi-Task and Lifelong Learning of Kernels. Vol. 9355, Springer, 2015, pp. 194–208, doi:10.1007/978-3-319-24486-0_13.
LibreCat: https://research-explorer.ista.ac.at/record/1706
DOI: 10.1007/978-3-319-24486-0_13

Shah, Neel, et al. A Multi-Plane Block-Coordinate Frank-Wolfe Algorithm for Training Structural SVMs with a Costly Max-Oracle. IEEE, 2015, pp. 2737–45, doi:10.1109/CVPR.2015.7298890.
LibreCat: https://research-explorer.ista.ac.at/record/1859
DOI: 10.1109/CVPR.2015.7298890

Royer, Amélie, and Christoph Lampert. Classifier Adaptation at Prediction Time. IEEE, 2015, pp. 1401–09, doi:10.1109/CVPR.2015.7298746.
LibreCat: https://research-explorer.ista.ac.at/record/1860
DOI: 10.1109/CVPR.2015.7298746

Lampert, Christoph. Predicting the Future Behavior of a Time-Varying Probability Distribution. IEEE, 2015, pp. 942–50, doi:10.1109/CVPR.2015.7298696.
LibreCat: https://research-explorer.ista.ac.at/record/1858
DOI: 10.1109/CVPR.2015.7298696
arXiv: 1406.5362

Pentina, Anastasia, et al. Curriculum Learning of Multiple Tasks. IEEE, 2015, pp. 5492–500, doi:10.1109/CVPR.2015.7299188.
LibreCat: https://research-explorer.ista.ac.at/record/1857
DOI: 10.1109/CVPR.2015.7299188

Martius, Georg S., and Eckehard Olbrich. “Quantifying Self-Organizing Behavior of Autonomous Robots.”10.7551/978-0-262-33027-5-ch018__Proceedings of the 13th European Conference on Artificial Life, MIT Press, 2015, p. 78, doi:10.7551/978-0-262-33027-5-ch018.
LibreCat: https://research-explorer.ista.ac.at/record/12881
DOI: 10.7551/978-0-262-33027-5-ch018

Sharmanska, Viktoriia. Learning with Attributes for Object Recognition: Parametric and Non-Parametrics Views. Institute of Science and Technology Austria, 2015, doi:10.15479/at:ista:1401.
LibreCat: https://research-explorer.ista.ac.at/record/1401
DOI: 10.15479/at:ista:1401

Martius, Georg S., and Eckehard Olbrich. “Quantifying Emergent Behavior of Autonomous Robots.”10.3390/e17107266__Entropy, vol. 17, no. 10, MDPI, 2015, pp. 7266–97, doi:10.3390/e17107266.
LibreCat: https://research-explorer.ista.ac.at/record/1655
DOI: 10.3390/e17107266

Muelling, Katharina, et al. “Movement Templates for Learning of Hitting and Batting.”10.1007/978-3-319-03194-1_3__Learning Motor Skills, edited by Jens Kober and Jan Peters, vol. 97, Springer, 2014, pp. 69–82, doi:10.1007/978-3-319-03194-1_3.
LibreCat: https://research-explorer.ista.ac.at/record/1829
DOI: 10.1007/978-3-319-03194-1_3

Hernandez Lobato, Daniel, et al. “Mind the Nuisance: Gaussian Process Classification Using Privileged Noise.” Advances in Neural Information Processing Systems, vol. 1, no. January, Neural Information Processing Systems, 2014, pp. 837–45.
LibreCat: https://research-explorer.ista.ac.at/record/2033

Morvant, Emilie, et al. “Majority Vote of Diverse Classifiers for Late Fusion.”10.1007/978-3-662-44415-3_16__Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 8621, Springer, 2014, pp. 153–62, doi:10.1007/978-3-662-44415-3_16.
LibreCat: https://research-explorer.ista.ac.at/record/2057
DOI: 10.1007/978-3-662-44415-3_16
arXiv: 1404.7796

Kolesnikov, Alexander, et al. “Closed-Form Approximate CRF Training for Scalable Image Segmentation.”10.1007/978-3-319-10578-9_36__Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), edited by David Fleet et al., vol. 8691, no. PART 3, Springer, 2014, pp. 550–65, doi:10.1007/978-3-319-10578-9_36.
LibreCat: https://research-explorer.ista.ac.at/record/2171
DOI: 10.1007/978-3-319-10578-9_36

Khamis, Sameh, and Christoph Lampert. “CoConut: Co-Classification with Output Space Regularization.” Proceedings of the British Machine Vision Conference 2014, BMVA Press, 2014.
LibreCat: https://research-explorer.ista.ac.at/record/2173

Sydorov, Vladyslav, et al. “Deep Fisher Kernels – End to End Learning of the Fisher Kernel GMM Parameters.”10.1109/CVPR.2014.182__Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, IEEE, 2014, pp. 1402–09, doi:10.1109/CVPR.2014.182.
LibreCat: https://research-explorer.ista.ac.at/record/2172
DOI: 10.1109/CVPR.2014.182

Bellet, Aurélien, et al. “Learning a Priori Constrained Weighted Majority Votes.”10.1007/s10994-014-5462-z__Machine Learning, vol. 97, no. 1–2, Springer, 2014, pp. 129–54, doi:10.1007/s10994-014-5462-z.
LibreCat: https://research-explorer.ista.ac.at/record/2180
DOI: 10.1007/s10994-014-5462-z

Morvant, Emilie. Adaptation de Domaine de Vote de Majorité Par Auto-Étiquetage Non Itératif. Vol. 1, Elsevier, 2014, pp. 49–58.
LibreCat: https://research-explorer.ista.ac.at/record/2189

Pentina, Anastasia, and Christoph Lampert. A PAC-Bayesian Bound for Lifelong Learning. Vol. 32, ML Research Press, 2014, pp. 991–99.
LibreCat: https://research-explorer.ista.ac.at/record/2160

Kazmar, Tomas, et al. Drosophila Embryo Stage Annotation Using Label Propagation. IEEE, 2013, doi:10.1109/ICCV.2013.139.
LibreCat: https://research-explorer.ista.ac.at/record/2294
DOI: 10.1109/ICCV.2013.139

Sharmanska, Viktoriia, et al. Learning to Rank Using Privileged Information. IEEE, 2013, pp. 825–32, doi:10.1109/ICCV.2013.107.
LibreCat: https://research-explorer.ista.ac.at/record/2293
DOI: 10.1109/ICCV.2013.107

Lampert, Christoph, et al. “Attribute-Based Classification for Zero-Shot Learning of Object Categories.”10.1109/TPAMI.2013.140__IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 36, no. 3, IEEE, 2013, pp. 453–65, doi:10.1109/TPAMI.2013.140.
LibreCat: https://research-explorer.ista.ac.at/record/2516
DOI: 10.1109/TPAMI.2013.140

Quadrianto, Novi, et al. “The Supervised IBP: Neighbourhood Preserving Infinite Latent Feature Models.” Proceedings of the 29th Conference Uncertainty in Artificial Intelligence, AUAI Press, 2013, pp. 527–36.
LibreCat: https://research-explorer.ista.ac.at/record/2520

Chen, Chao, et al. Computing the M Most Probable Modes of a Graphical Model. Vol. 31, JMLR, 2013, pp. 161–69.
LibreCat: https://research-explorer.ista.ac.at/record/2901

Tommasi, Tatiana, et al. Beyond Dataset Bias: Multi-Task Unaligned Shared Knowledge Transfer. Vol. 7724, Springer, 2013, pp. 1–15, doi:10.1007/978-3-642-37331-2_1.
LibreCat: https://research-explorer.ista.ac.at/record/2948
DOI: 10.1007/978-3-642-37331-2_1

Quadrianto, Novi, and Christoph Lampert. “Kernel Based Learning.”10.1007/978-1-4419-9863-7_604__Encyclopedia of Systems Biology, edited by Werner Dubitzky et al., vol. 3, Springer, 2013, pp. 1069–1069, doi:10.1007/978-1-4419-9863-7_604.
LibreCat: https://research-explorer.ista.ac.at/record/3321
DOI: 10.1007/978-1-4419-9863-7_604

Lampert, Christoph. Dynamic Pruning of Factor Graphs for Maximum Marginal Prediction. Vol. 1, Neural Information Processing Systems, 2012, pp. 82–90.
LibreCat: https://research-explorer.ista.ac.at/record/2825

Blaschko, Matthew, and Christoph Lampert. “Guest Editorial: Special Issue on Structured Prediction and Inference.”10.1007/s11263-012-0530-y__International Journal of Computer Vision, vol. 99, no. 3, Springer, 2012, pp. 257–58, doi:10.1007/s11263-012-0530-y.
LibreCat: https://research-explorer.ista.ac.at/record/3164
DOI: 10.1007/s11263-012-0530-y

Sharmanska, Viktoriia, et al. Augmented Attribute Representations. Vol. 7576, no. PART 5, Springer, 2012, pp. 242–55, doi:10.1007/978-3-642-33715-4_18.
LibreCat: https://research-explorer.ista.ac.at/record/3125
DOI: 10.1007/978-3-642-33715-4_18

Müller, Andreas, et al. Information Theoretic Clustering Using Minimal Spanning Trees. Vol. 7476, Springer, 2012, pp. 205–15, doi:10.1007/978-3-642-32717-9_21.
LibreCat: https://research-explorer.ista.ac.at/record/3126
DOI: 10.1007/978-3-642-32717-9_21

Lampert, Christoph, and Jan Peters. “Real-Time Detection of Colored Objects in Multiple Camera Streams with off-the-Shelf Hardware Components.”10.1007/s11554-010-0168-3__Journal of Real-Time Image Processing, vol. 7, no. 1, Springer, 2012, pp. 31–41, doi:10.1007/s11554-010-0168-3.
LibreCat: https://research-explorer.ista.ac.at/record/3248
DOI: 10.1007/s11554-010-0168-3

Korc, Filip, et al. Approximating Marginals Using Discrete Energy Minimization. ICML, 2012.
LibreCat: https://research-explorer.ista.ac.at/record/3124

Korc, Filip, et al. Approximating Marginals Using Discrete Energy Minimization. IST Austria, 2012, doi:10.15479/AT:IST-2012-0003.
LibreCat: https://research-explorer.ista.ac.at/record/5396
DOI: 10.15479/AT:IST-2012-0003

Kroemer, Oliver, et al. Multi-Modal Learning for Dynamic Tactile Sensing. Deutsches Zentrum für Luft und Raumfahrt, 2012.
LibreCat: https://research-explorer.ista.ac.at/record/2915

Quadrianto, Novi, et al. “The Most Persistent Soft-Clique in a Set of Sampled Graphs.” Proceedings of the 29th International Conference on Machine Learning, ML Research Press, 2012, pp. 211–18.
LibreCat: https://research-explorer.ista.ac.at/record/3127

Wang, Zhikun, et al. Learning Anticipation Policies for Robot Table Tennis. IEEE, 2011, pp. 332–37, doi:10.1109/IROS.2011.6094892.
LibreCat: https://research-explorer.ista.ac.at/record/3337
DOI: 10.1109/IROS.2011.6094892

Blaschko, Matthew, et al. “Semi Supervised Kernel Canonical Correlation Analysis with Application to Human FMRI.”10.1016/j.patrec.2011.02.011__Pattern Recognition Letters, vol. 32, no. 11, Elsevier, 2011, pp. 1572–83, doi:10.1016/j.patrec.2011.02.011.
LibreCat: https://research-explorer.ista.ac.at/record/3389
DOI: 10.1016/j.patrec.2011.02.011

Kroemer, Oliver, et al. “Learning Dynamic Tactile Sensing with Robust Vision Based Training.”10.1109/TRO.2011.2121130__IEEE Transactions on Robotics, vol. 27, no. 3, IEEE, 2011, pp. 545–57, doi:10.1109/TRO.2011.2121130.
LibreCat: https://research-explorer.ista.ac.at/record/3382
DOI: 10.1109/TRO.2011.2121130

Chen, Chao, et al. Enforcing Topological Constraints in Random Field Image Segmentation. IST Austria, 2011, doi:10.15479/AT:IST-2011-0002.
LibreCat: https://research-explorer.ista.ac.at/record/5386
DOI: 10.15479/AT:IST-2011-0002

Chen, Chao, et al. “Enforcing Topological Constraints in Random Field Image Segmentation.”10.1109/CVPR.2011.5995503__CVPR: Computer Vision and Pattern Recognition, IEEE, 2011, pp. 2089–96, doi:10.1109/CVPR.2011.5995503.
LibreCat: https://research-explorer.ista.ac.at/record/3336
DOI: 10.1109/CVPR.2011.5995503

Lampert, Christoph. Maximum Margin Multi-Label Structured Prediction. Neural Information Processing Systems, 2011.
LibreCat: https://research-explorer.ista.ac.at/record/3163

Lampert, Christoph. “Maximum Margin Multi Label Structured Prediction.” NIPS: Neural Information Processing Systems, Neural Information Processing Systems Foundation, 2011.
LibreCat: https://research-explorer.ista.ac.at/record/3322

Nowozin, Sebastian, and Christoph Lampert. “Structured Learning and Prediction in Computer Vision.”10.1561/0600000033__Foundations and Trends in Computer Graphics and Vision, vol. 6, no. 3–4, Now Publishers, 2011, pp. 185–365, doi:10.1561/0600000033.
LibreCat: https://research-explorer.ista.ac.at/record/3320
DOI: 10.1561/0600000033

Quadrianto, Novi, and Christoph Lampert. Learning Multi-View Neighborhood Preserving Projections. ML Research Press, 2011, pp. 425–32.
LibreCat: https://research-explorer.ista.ac.at/record/3319

Lampert, Christoph, and Oliver Krömer. Weakly-Paired Maximum Covariance Analysis for Multimodal Dimensionality Reduction and Transfer Learning. Vol. 6312, Springer, 2010, pp. 566–79, doi:10.1007/978-3-642-15552-9_41.
LibreCat: https://research-explorer.ista.ac.at/record/3794
DOI: 10.1007/978-3-642-15552-9_41

Nowozin, Sebastian, et al. On Parameter Learning in CRF-Based Approaches to Object Class Image Segmentation. Vol. 6316, Springer, 2010, pp. 98–111, doi:10.1007/978-3-642-15567-3_8.
LibreCat: https://research-explorer.ista.ac.at/record/3793
DOI: 10.1007/978-3-642-15567-3_8